Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R² = 0.35–0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R² = 0.90) in predicting soil quality compared to SQI_p (R² = 0.23).

The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in ‘prospectr’ R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R² > 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms.

Plant roots are an integral part of soil ecosystems and contribute to various services, including carbon and nutrient cycling, weathering, and soil formation. They also modify soil physical properties (e.g., soil water content, pore size distribution, and bulk density) and impact subsequent crops’ growth. Cover crops have been reported to improve soil and environmental quality by reducing nutrient losses, improving soil water content, and increasing soil organic matter. Understanding the complex interactions between cover crop roots and soil (RS) is of utmost importance. However, cover crop RS interactions have not been critically reviewed. In this article, we investigated the nature of cover crop physical RS interactions and explored the emerging technologies for their study. We also assessed technologies that may be readily applied to the study of physical RS interactions in cover crop systems and discussed ways to improve related research in the future.

The soil water retention curve (SWRC) shows the relationship between soil water (θ) and water potential (ψ) and provides fundamental information for quantifying and modeling soil water entry, storage, flow, and groundwater recharge processes. While traditionally it is measured in a laboratory through cumbersome and time-intensive methods, soil sensors measuring in-situ θ and ψ show strong potential to estimate in-situ SWRC. The objective of this study was to estimate in-situ SWRC at different depths under two different soil types by integrating measured θ and ψ using two commercial sensors: time-domain reflectometer (TDR) and dielectric field water potential (e.g., MPS-6) principles. Parametric models were used to quantify θ—ψ relationships at various depths and were compared to laboratory-measured SWRC. The results of the study show that combining TDR and MPS-6 sensors can be used to estimate plant-available water and SWRC, with a mean difference of −0.03 to 0.23 m³m⁻³ between the modeled data and laboratory data, which could be caused by the sensors’ lack of site-specific calibration or possible air entrapment of field soil. However, consistent trends (with magnitude differences) indicated the potential to use these sensors in estimating in-situ and dynamic SWRC at depths and provided a way forward in overcoming resource-intensive laboratory measurements.

While the capability of integrated nutrient management (INM) in rice systems has been adequately studied, little is known about the related short-term carbon sequestration and changes in soil carbon fractions. Our study examined the responses of organic carbon pools, carbon sequestration and rice yields after application of different organic manures combined with chemical fertilizers in a rice–rice (Oryza sativa L.) cropping system in the red and laterite agro-climatic zones of West Bengal, India. The treatments included non-fertilized control; rice straw (RS) + nitrogen, phosphorus and potassium fertilizer (NPK); Gliricidia (GL) + NPK; farmyard manure (FYM) + NPK; vermicompost (VC) + NPK; and NPK only. Rice straw + NPK treatment resulted in the highest total organic carbon and passive pool of carbon. Vermicompost + NPK treatment resulted in the highest oxidizable organic carbon (0.69%), dissolved organic carbon (0.007%) and microbial biomass carbon (0.01%), followed by FYM + NPK, GL + NPK and RS + NPK as compared to control. Rice straw + NPK sequestered the highest amount of carbon dioxide (CO₂) as the total organic carbon (91.10 t ha⁻¹) and passive pool of carbon (85.64 t ha⁻¹), whereas VC + NPK resulted in the highest amount of CO₂ (10.24 t ha⁻¹) being sequestered as the active pool of carbon, followed by FYM + NPK (8.33 t ha⁻¹) and GL + NPK (7.22 t ha⁻¹). The application of both NPK only and VC + NPK treatments resulted in the highest grain yields over the three cropping seasons. In spite of high carbon sequestration being observed in more recalcitrant carbon pools, RS + NPK resulted in little increase (3.52 t ha⁻¹) in rice yield over the short term. The results of this study suggest that the short-term changes of soil carbon fractions and carbon sequestration primarily depend on the type of organic manure used. Vermicompost, FYM and GL provide more labile carbon, which can improve rice yield over the short term. However, it is suggested to explore the dynamics of different carbon fractions, carbon sequestration in different pools and rice yields over longer periods of time.